Massive multiple-input-multiple-output (M-MIMO) technology has been proposed to support ultra reliability and low latency (URLLC) data transmissions. The reduction of minimum transmission time intervals (TTI) and bit-error-rate (before coding) requirements
from 15 ms and \(10^{-3}\)  to 1 ms and \(10^{-5}\)  in 4G and 5G NR URLLC [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, respectively, pose a challenging problem in developing an M-MIMO receiver.
Two types of M-MIMO receivers have been investigated in the literature; classical and Bayesian learning receivers.
